High baudrate data transmission may generally encounter intersymbol interference (ISI), where subsequent symbols interfere with or overlap one another, making reliable reception difficult or even impossible. The ISI might arise from amplitude or phase distortion, or in the case of digital radio communication, from multipath propagation.
The mentioned types of distortion are linear and may hence be combatted with a channel equalizer. Typically this equalizer is implemented as a tapped delay line, finite impulse response filter, where the taps or coefficients are adjustable, thus making it possible to adjust the filter characteristics.
There are a number of methods for determining the coefficients for a channel equalizer. It is known that an optimal equalizer can be obtained from a solution of the matrix equation: EQU RC=P, (1)
where C is a N.times.1 vector in which the elements are the equalizer coefficients, R is a NxN channel correlation matrix, and P is a N.times.1 cross-correlation vector of samples received and symbols sent.
In devices typically employing adaptive equalizers, such as data modems, it is found that the value N is required to be large. As a result, a direct solution of Equation (1) to obtain an optimal equalizer requires a large amount of calculation capacity. However, in that the required equalizer adaptability is generally minimal, relatively simple iterative methods, such as a Least Mean Squared (LMS) technique, can be employed for solving for suboptimal equalizer coefficients.
When optimal equalizer coefficients are desired, as opposed to suboptimal equalizer coefficients, it has been known to solve Equation 1 iteratively or recursively by RLS or Kalman algorithms. However, these algorithms, in their basic form, suffer from a sensitivity to a representation accuracy (number of bits) of the numbers used in the calculation. Unfortunately, decreasing the sensitivity also rapidly increases the requirement for greater calculation capacity.
In addition, all of the known iterative methods are carried out each time a data symbol is received, otherwise the equalizer adaptability is adversely affected.
It is known that a direct solution of Equation 1 may be accomplished by inverting the matrix R and multiplying the matrices R.sup.-1 and P, or by solving the equation group formed by the matrices, of which the latter is a more efficient approach.
It is an object of this invention to provide an improved method, and apparatus for accomplishing the method, for updating correction coefficients for an adaptive equalizer.